Dataset analysis platform Appen has acquired Figure Eight, a developer of machine learning technology, in a deal which could be worth $300m.
Appen will initially pay $175m up-front for Figure Eight, but an additional payment of up to $125m will be paid in 2020, depending on the performance during 2019.
In acquiring Figure Eight, Appen is hoping to build an end-to-end training data offering.
Until the end of 2019, Figure Eight is set to operate as a largely independent division of Appen, in a bid to ensure it remains focused on its product development and customers. Beyond 2019, the Figure Eight team and technology will be ‘integral to Appen’s future success’, the company said.
San Francisco-based Figure Eight is a machine learning platform which transforms audio, text, video and image data into training data which can be used to support machine learning technology with use cases such as consumer product identification, natural language processing, or intelligent chatbots, among others.
Australia-based Appen offers data annotation, collection, evaluation and transcription to build a record of image, speech, text and video data, which can then be used by AI and machine learning technology. Companies operating within the finance, automotive, healthcare, government and retail industries leverage the solution to secure their data and build improved products and services which use natural language and machine learning.
Some of Appen’s solutions include fraud detection, data analytics, risk management, proofing, automatic speech recognition, and machine translation, among others. To support anti-fraud teams, Appen can identify fraudulent transactions or help an insurance company detect false claims.
Appen CEO Mark Brayan said, “The union of Appen and Figure Eight creates a unique, exciting and powerful opportunity for our customers. We now have the best of both worlds: our highly efficient cloud management platform and scalable, skilful, multilingual crowd, combined with Figure Eight’s innovative customer-facing SaaS platform with ML-assisted annotation. Combined, we’ll meet and exceed our customers’ scale, speed and quality requirements.”
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